Papers
15
Total Citations
233
H-Index
8
About
Bailu Si is a computational neuroscientist and robotics researcher whose work sits at the intersection of brain-inspired computing and autonomous robot navigation. Drawing on neurobiological principles from the entorhinal-hippocampal system, Si has made significant contributions to cognitive mapping, simultaneous localization and mapping (SLAM), and robotic exploration. His landmark work, "NeuroBayesSLAM" (2020, 52 citations), demonstrated how Bayesian integration of multisensory information—modeled on mammalian neural circuits—can dramatically improve robot navigation in complex environments. Earlier, his cognitive mapping model incorporating conjunctive representations of space and movement (2017, 34 citations) offered a biologically grounded solution to robust SLAM in large-scale dynamic settings. Beyond navigation, Si has advanced reinforcement learning-based control for robotic manipulators (2018, 38 citations) and developed efficient frontier-detection algorithms that improve autonomous robot exploration. His DIAMOND model further extends brain-inspired deep recurrent architectures to sensorimotor control. Spanning over two decades of research, Si's body of work consistently bridges neuroscience and robotics, providing the field with computationally efficient, biologically plausible frameworks that continue to influence both academic research and practical robot autonomy.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Reinforcement Learning Neural Network for Robotic Manipulator Control38 citations · 2018
- 3Cognitive Mapping Based on Conjunctive Representations of Space and Movement34 citations · 2017
- 4A brain-inspired compact cognitive mapping system23 citations · 2020
- 5
- 6Sample-Based Frontier Detection for Autonomous Robot Exploration17 citations · 2018
- 7A sampling-based multi-tree fusion algorithm for frontier detection15 citations · 2019
- 8
- 9
- 10Spatiotemporal Dual-Stream Network for Visual Odometry5 citations · 2025